Buildings and Cities

Smart Thermostats

Thermostats are mission control for residential energy use for heating and cooling—9 percent of energy consumption in the United States. At present, the majority of thermostats require manual operation or preset programming, and studies show people are notoriously unreliable in doing either efficiently. Smart thermostats eliminate the capriciousness of human behavior, thereby driving more predictable energy savings.

The first smart thermostat, the Nest, came to market in 2011, developed by a team of former iPhone engineers who saw an opportunity to bring smartphone thinking to the antiquated temperature controls in homes. Thanks to algorithms and sensors, next-generation thermostats learn over time by gathering and analyzing data. You can still turn the temperature up and down, but these devices will remember your choices and memorize your routines—adapting to the dynamic nature of day-to-day living.

Smart thermostats detect occupancy, learn inhabitants’ preferences, and nudge users toward more efficient behavior. The newest technologies also integrate demand response; they can reduce consumption at times of peak energy use, peak prices, and peak emissions. The net effect: Residences are more energy efficient, more comfortable, and less costly to operate.

#57

Rank and Results by 2050

2.62 gigatonsreduced CO2

$74.16 Billionnet implementation cost

$640.1 Billionnet operational savings

Impact: We project that smart thermostats could grow from .4 percent to 46 percent of households with internet access by 2050. In this scenario, 704 million homes would have them. Reduced energy use could avoid 2.6 gigatons of carbon dioxide emissions. Return on investment is high: smart thermostats can save their owners $640 billion on utility bills by 2050.

References

European Union’s energy use [for heating and cooling]: European Commission. An EU Strategy on Heating and Cooling. Brussels, February 16, 2016.

Technical Summary

Smart Thermostats

Project Drawdown defines smart thermostats as: Internet-connected devices in households that reduce the heating and cooling demand of homes by using sensors and intelligent settings to maintain building comfort. This solution replaces conventional home thermostats.

The built environment accounts for a substantial portion of global energy consumption and expense, and there is significant room for improvement in how the energy used to heat and cool buildings is managed, especially in the residential sector. Less than half of homeowners actively manage their energy use, and the vast majority of thermostats currently on the market do not offer energy saving benefits.

In recent years, a new type of thermostat has been introduced—a “smart thermostat”, “learning thermostat”, or “wi-fi thermostat”. This type connects to the Internet to allow settings control from anywhere, and learns from user behavior to optimize energy settings, saving 10-15 percent of energy needs while improving comfort and convenience.

The analysis below examines the financial and emissions impacts of high adoption of smart thermostats instead of non-programmable and programmable thermostats over the period 2020-2050.

Much of the energy savings benefits of smart thermostats requires Internet at home, so for the total addressable market, the number of Internet-enabled households was used. This was calculated by estimating the relationship between the number of Internet-enabled households and the global average gross domestic product (GDP) per capita using United Nations data (UN, 2001). Projections for GDP per capita [2] were then used to project how the number of Internet-enabled households would change over 2020-2050. This indicated, for instance, 909 million Internet-enabled households in 2014.

Current adoption [3] of smart thermostats is roughly 0.34 percent of the market globally, primarily in the US, Europe, and Asia Pacific (Berg Insight, 2016).

Impacts of increased adoption of smart thermostats from 2020-2050 were generated based on three growth scenarios, which were developed by estimating the relationship between GDP per capita and the number of Internet-enabled households [5] and using that relationship to calculate adoption for several projections. Each scenario was assessed in comparison to a Reference Scenario, where the solution’s market share was fixed at the current levels.

Plausible Scenario: This scenario uses one standard deviation below the average projected adoption for each year.

Drawdown Scenario: This scenario uses the average of all projections for each year.

Optimum Scenario: This scenario uses one standard deviation above the average projected adoption for each year.

Emissions Model

Emissions numbers came from grid emissions factors and natural gas emissions factors using data from the Intergovernmental Panel on Climate Change (IPCC).

Financial Model

First costs for conventional and smart thermostats were derived from the averages of 16 and 11 data points, respectively, from retailer websites covering the US, UK, and EU. No installation costs were assumed. This produced an average conventional price of US$39 and an average smart thermostat price of US$229. [6] A 13 percent learning rate was applied to the solution, which came from data on air conditioners. Operating costs were taken as the electricity cost for cooling and fuel costs of heating homes using data for the US, EU, and China (US EIA, 2016).

The integration process first had each building solution prioritized according to the point of impact on building energy usage (with building envelope solutions first, building applications like HVAC second, and building systems like smart thermostats last). The energy savings potential of smart thermostats was reduced to represent the prior energy savings of higher priority solutions. Energy reductions from smart thermostat use averaged to 12.8 percent for cooling and 8.9 percent for heating, after adjustments for integration effects.

Results

The Plausible Scenario forecasts that 704 million households will have installed a smart thermostat by 2050. The climate and financial impacts for this accelerated adoption of smart thermostats are significant: 2.6 gigatons of carbon dioxide-equivalent greenhouse gas emissions avoided, equivalent to approximately 0.23 parts per million. The marginal capital cost of this would be US$74 billion, but it would save US$640 billion in operating costs due to reduced energy consumption for space heating and cooling. Based on the financial impacts alone, it is clear that global adoption of smart thermostats is economically viable and will provide a significant return on investment. The impacts of the Drawdown Scenario and Optimum Scenario are higher, at 4.28 gigatons and 9.77 gigatons, respectively.

Discussion

The benefits that smart thermostats provide are substantial enough that it is not unlikely that they will become a replacement technology for mechanical or programmable thermostats, although their widespread adoption will take time.

The high upfront cost of smart thermostats has inhibited growth, and there are several other barriers to adoption, including access to Internet and appropriate heating and cooling systems. In many developing countries with warm climates, the lack of centralized digital air-conditioning systems inhibits high adoption, but the data collected indicates that the financial benefits are similar between heating and cooling, so smart thermostats could grow with the increased use of newer air condition systems.

As new competitors enter the market, the price of smart thermostats is expected to drop, and as rates of household Internet access and centralized heating and cooling systems grow, adoption will accelerate. Growth could be further driven with government and utility support and through the development of programs that demonstrate to consumers the benefits of smart thermostats.

[1] For more on the Total Addressable Market for the Buildings and Cities Sector, click the Sector Summary: Buildings and Cities link below.

[3] Current adoption is defined as the amount of functional demand supplied by the solution in the base year of study. This study uses 2014 as the base year due to the availability of global adoption data for all Project Drawdown solutions evaluated.

[4] To learn more about Project Drawdown’s three growth scenarios, click the Scenarios link below. For information on Buildings and Cities Sector-specific scenarios, click the Sector Summary: Buildings and Cities link.

[5] Using adoption data from Berg Insight (2015) and GDP per capita data for several scenarios from the AMPERE MESSAGE3 model.